Typically, in IP networks of managed devices, such as automated teller machines (ATMs), a simple network management protocol (SNMP) agent runs on each device in the network. These SNMP agents collect performance metric data relating to the operation of the managed devices and send alert messages when a device malfunctions.
Typically, the performance metric data collected by an SNMP agent is in a proprietary format. These proprietary data formats require translation by the SNMP agent into an SNMP compatible form for transmission across a communications network to a network management system (NMS). The NMS runs applications that manage and control the managed devices.
In the case of a network of ATMs, each ATM runs an agent, for example an SNMP agent that monitors a number of performance metrics associated with the operation of the ATM including, inter alia, the downtime of each ATM. It will be understood that the term downtime is used herein to refer to time during which the ATM cannot be used for carrying out transactions, either withdrawal or deposit.
Typically, older ATMs, sometimes referred to a “legacy systems”, monitor downtime using system network architecture (SNA) in which the ATM itself keeps a log of their usage, status and how many operations have taken place in a specified time period, for example how many receipts have been printed since the thermal paper roll was last changed?
Such monitoring of the down time of the ATMs gives a measure of the reliability of the ATM network. However, a simple ATM downtime measurement, in terms of hours and/or minutes, does not give a true measure of the business impact of the ATM downtime on a financial institution. For example, a customer who finds an ATM that is down may complete a transaction in branch, thereby incurring the additional cost for the financial institution of the human involvement of a member of staff in the transaction.
Alternatively, the customer may complete their transaction at the ATM of a second financial institution. The completion of the transaction at the second financial institution's ATM may incur a cost to the first financial institution if the transaction was completed by a customer of the first financial institution, as it becomes a “not on us” transaction. Alternatively, the completion of the transaction at the second financial institution's ATM may reduce charges levied by the first financial institution if the transaction was completed by a customer of another financial institution, as a “not on us” transaction is lost by the first financial institution.
Downtime occurring during an ATM's high usage period, for example lunchtime the day following Thanksgiving, has a greater impact upon the number of customers who cannot be serviced, than downtime during a low usage period or at a low usage ATM. However, using time based current metrics downtime at high usage ATMs, or during high usage periods, contributes equally to an overall downtime measurement as downtime occurring at low usage periods, for example two a.m. on Thanksgiving morning. This is despite the great disparity in the numbers of customers that this downtime would affect.
Recurring instances of ATM downtime at high usage periods affects customer satisfaction, and can ultimately reduce customer retention.
Using current metrics it is not possible to schedule maintenance routes based upon the ATMs currently down that have the greatest customer impact.
Currently, there is no integrated environment in which key performance indicators (KPIs) can be monitored and tracked. This results in a lack of coherent planning and scheduling based upon KPIs.